Cross B-HUB Based RNN with Random Aural-Feature Extraction for Enhanced Speaker Extraction and Speaker Recognition
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-022-10096-3.pdf
Reference26 articles.
1. Zulfiqar, Ali, & Aslam Muhammad, A. M. (2009). Martinez Enriquez, A speaker identification system using MFCC features with VQ technique. In 2009 Third International Symposium on Intelligent Information Technology Application IEEE, 3, 115–118.
2. Kumar, C., ur Rehman, F., Kumar, S., Mehmood, A., & Shabir, G. (2018). Analysis of MFCC and BFCC in a speaker identification system. In 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) IEEE, 1–5.
3. Lukic, Y., Vogt, C., Dürr, O., & Stadelmann, T. (2016). Speaker identification and clustering using convolutional neural networks. In 2016 IEEE 26th international workshop on machine learning for signal processing (MLSP) IEEE, 1–6.
4. Mamun, Nursadul, Ria Ghosh & John Hansen, H. L. (2019). Quantifying cochlear implant users' ability for speaker identification using CI Auditory Stimuli. arXiv preprint arXiv:1908.00031.
5. Fan, Lei, Qing-Yuan Jiang, Ya-Qi Yu & Wu-Jun Li. (2019). Deep Hashing for Speaker Identification and Retrieval. In Proceeding Interspeech,(pp. 2908–2912).
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